RACR: Reasoning-Aware Context Reconstruction

From Noise to Logic: Reasoning-aware Context Reconstruction for Multi-hop QA Submitted to CIKM 2026 (Submission #840)

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Overview

RACR is a two-stage context compression framework for Retrieval-Augmented Generation (RAG) that addresses two structural mismatches in deep retrieval scenarios:

1. Selection Mismatch (Bridge Evidence Problem): Bridge evidence — intermediate facts essential for multi-hop chains but weakly similar to the query — is discarded by traditional relevance-based methods.
2. Organization Mismatch (Logical Structure Problem): Retrieved snippets are ordered by retrieval rank, not logical precedence, breaking causal chains.

RACR decouples these concerns into two stages:

• Stage 1 — Reasoning-Aware Atomic Refinement ([Mode] ANALYSIS) Groups snippets into bounded windows and distils each into concise, utility-validated notes.
• Stage 2 — Global Logical Synthesis ([Mode] REFLECTION) Constructs a coherent deductive chain from the evidence pool and filters off-chain redundancies.

Repository Structure

RACR/
+-- racr/               # Core RACR module
|   +-- base.py         # BaseCompressor interface and SearchResult dataclass
|   +-- compressor.py   # RACRCompressor (two-stage Analysis + Reflection)
|   +-- prompt.py       # ANALYSIS_TEMPLATE and REFLECTION_TEMPLATE
|   +-- utils.py        # Text chunking and JSON parsing helpers
+-- baselines/          # Baseline compressors used in the paper
|   +-- compact/        # CompAct
|   +-- refiner/        # Refiner
|   +-- recomp_abst/    # RECOMP-Abstractive
|   +-- recomp_extr/    # RECOMP-Extractive
|   +-- longllmlingua/  # LongLLMLingua
+-- evaluate/
|   +-- run_eval.py     # Main evaluation entry point
|   +-- metrics.py      # EM / F1 scoring
+-- configs/
|   +-- eval_config.py  # Dataset paths and model settings
+-- requirements.txt

Checkpoint and Data Release

Note (Anonymous Submission Stage) The fine-tuned model checkpoint and training data are not included in this submission to comply with double-blind review requirements.

Upon acceptance, we will publicly release:

• The fine-tuned Qwen2.5-7B-Instruct RACR checkpoint on Hugging Face Hub.
• The full distillation dataset (~78k samples) synthesized via the DeepSeek-R1 teacher pipeline.
• Step-by-step training scripts and data synthesis pipeline.

Quick Start

1. Install dependencies

pip install -r requirements.txt

2. Prepare retrieval data

Each dataset file should be a JSONL where every line follows:

{"question": "...", "answers": ["..."], "ctxs": [{"title": "...", "text": "..."}]}

Update paths in configs/eval_config.py.

3. Set the RACR model path

In configs/eval_config.py, set racr_model_path to the released checkpoint path (available after acceptance).

4. Run evaluation

cd RACR
python evaluate/run_eval.py

Results (EM, F1, Compression Ratio) are saved to the directory specified in eval_config.py.

Programmatic Usage

from racr import RACRCompressor, SearchResult

compressor = RACRCompressor(
    model_path="<path_to_checkpoint>",
    use_vllm=True,
    vllm_tensor_parallel_size=1,
)

docs = [
    SearchResult(evi_id=0, docid=0, title="Doc A", text="..."),
    SearchResult(evi_id=1, docid=1, title="Doc B", text="..."),
]

compressed = compressor.compress(query="Who founded SpaceX?", documents=docs)
print(compressed[0].text)

Main Results (Top-50 Retrieval)

Method	NQ EM	TQA EM	HotpotQA EM	2Wiki EM	MuSiQue EM	Avg EM	CR
Original	30.1	61.1	28.5	22.8	18.4	32.2	1x
LongLLMLingua	28.2	59.8	29.5	24.6	15.8	31.6	9x
CompAct	33.9	59.7	30.2	20.7	16.9	32.3	35x
Refiner	28.4	56.6	27.3	20.4	18.4	30.2	46x
RACR (Ours)	35.0	60.3	32.6	29.0	36.1	38.6	41x

Improvements are statistically significant at p < 0.01 on all multi-hop datasets.

License

This code is released under the MIT License.